Title: A novel supervised feature extraction algorithm: enhanced within-class linear discriminant analysis

Authors: Di Zhang; Yun Zhao; Minghui Du

Addresses: School of Information Engineering, Guangdong Medical College, Dongguan, Guangdong 523808, China; School of Electronics and Information, South China University of Technology, Guangzhou, Guangdong 510641, China ' School of Information Engineering, Guangdong Medical College, Dongguan, Guangdong 523808, China ' School of Electronics and Information, South China University of Technology, Guangzhou, Guangdong 510641, China

Abstract: Linear discriminant analysis (LDA) is one of the most popular supervised feature extraction techniques used in machine learning and pattern classification. However, LDA only captures global structure information of the data and ignores the structure information of local data points. In this paper, a novel supervised feature extraction algorithm called enhanced within-class linear discriminant analysis (EWLDA) is proposed. More specifically, we define a local within-class scatter matrix to model the local structure information provided by local data samples. In order to balance the tradeoff between global and local structure information, a tuning parameter is also introduced. Experimental results on two image databases demonstrate the effectiveness of our algorithm.

Keywords: feature extraction; linear discriminant analysis; LDA; global information; local structure information; pattern classification; within-class scatter matrix; modelling; tuning parameters.

DOI: 10.1504/IJCSE.2016.077729

International Journal of Computational Science and Engineering, 2016 Vol.13 No.1, pp.13 - 23

Received: 24 May 2013
Accepted: 11 Aug 2013
Published online: 14 Jul 2016 *

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